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Added some documentation.

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This commit is contained in:
Dan Crankshaw 2013-10-18 15:11:21 -07:00
parent 3f3d28c73f
commit 3a40a5eb30
7 changed files with 83 additions and 68 deletions
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33
graph/src/main/scala/org/apache/spark/graph/Graph.scala
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@ -184,13 +184,42 @@ abstract class Graph[VD: ClassManifest, ED: ClassManifest] {
/**
* @todo document function
* groupEdgeTriplets is used to merge multiple edges that have the
* same source and destination vertex into a single edge. The user
* supplied function is applied to each directed pair of vertices (u, v) and
* has access to all EdgeTriplets
*
* {e: for all e in E where e.src = u and e.dst = v}
*
* This function is identical to [[org.apache.spark.graph.Graph.groupEdges]]
* except that this function
* provides the user-supplied function with an iterator over EdgeTriplets,
* which contain the vertex data, whereas groupEdges provides the user-supplied
* function with an iterator over Edges, which only contain the vertex IDs.
*
* @tparam ED2 the type of the resulting edge data after grouping
*
* @param f the user supplied function to merge multiple EdgeTriplets
* into a single ED2 object
*
* @return Graph[VD,ED2] The resulting graph with a single Edge for each
* source, dest vertex pair.
*
*/
def groupEdgeTriplets[ED2: ClassManifest](f: Iterator[EdgeTriplet[VD,ED]] => ED2 ): Graph[VD,ED2]
/**
* @todo document function
* This function merges multiple edges between two vertices into a single
* Edge. See [[org.apache.spark.graph.Graph.groupEdgeTriplets]] for more detail.
*
* @tparam ED2 the type of the resulting edge data after grouping.
*
* @param f the user supplied function to merge multiple Edges
* into a single ED2 object.
*
* @return Graph[VD,ED2] The resulting graph with a single Edge for each
* source, dest vertex pair.
*/
def groupEdges[ED2: ClassManifest](f: Iterator[Edge[ED]] => ED2 ): Graph[VD,ED2]

2
graph/src/main/scala/org/apache/spark/graph/GraphLab.scala
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@ -4,7 +4,7 @@ import scala.collection.JavaConversions._
import org.apache.spark.rdd.RDD
/**
* This object implement the graphlab gather-apply-scatter api.
* This object implements the GraphLab gather-apply-scatter api.
*/
object GraphLab {

19
graph/src/main/scala/org/apache/spark/graph/GraphLoader.scala
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@ -9,7 +9,18 @@ import org.apache.spark.graph.impl.GraphImpl
object GraphLoader {
/**
* Load an edge list from file initializing the Graph RDD
* Load an edge list from file initializing the Graph
*
* @tparam ED the type of the edge data of the resulting Graph
*
* @param sc the SparkContext used to construct RDDs
* @param path the path to the text file containing the edge list
* @param edgeParser a function that takes an array of strings and
* returns an ED object
* @param minEdgePartitions the number of partitions for the
* the Edge RDD
*
* @todo remove minVertexPartitions arg
*/
def textFile[ED: ClassManifest](
sc: SparkContext,
@ -38,14 +49,10 @@ object GraphLoader {
}.cache()
val graph = fromEdges(edges)
// println("Loaded graph:" +
// "\n\t#edges: " + graph.numEdges +
// "\n\t#vertices: " + graph.numVertices)
graph
}
def fromEdges[ED: ClassManifest](edges: RDD[Edge[ED]]): GraphImpl[Int, ED] = {
private def fromEdges[ED: ClassManifest](edges: RDD[Edge[ED]]): GraphImpl[Int, ED] = {
val vertices = edges.flatMap { edge => List((edge.srcId, 1), (edge.dstId, 1)) }
.reduceByKey(_ + _)
.map{ case (vid, degree) => (vid, degree) }

5
graph/src/main/scala/org/apache/spark/graph/GraphOps.scala
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@ -94,11 +94,6 @@ class GraphOps[VD: ClassManifest, ED: ClassManifest](graph: Graph[VD, ED]) {
} // end of aggregateNeighbors
def collectNeighborIds(edgeDirection: EdgeDirection) : RDD[(Vid, Array[Vid])] = {
val nbrs = graph.aggregateNeighbors[Array[Vid]](
(vid, edge) => Some(Array(edge.otherVertexId(vid))),

29
graph/src/main/scala/org/apache/spark/graph/Pregel.scala
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@ -3,8 +3,37 @@ package org.apache.spark.graph
import org.apache.spark.rdd.RDD
/**
* This object implements the Pregel bulk-synchronous
* message-passing API.
*/
object Pregel {
/**
* Execute the Pregel program.
*
* @tparam VD the vertex data type
* @tparam ED the edge data type
* @tparam A the Pregel message type
*
* @param vprog a user supplied function that acts as the vertex program for
* the Pregel computation. It takes the vertex ID of the vertex it is running on,
* the accompanying data for that vertex, and the incoming data and returns the
* new vertex value.
* @param sendMsg a user supplied function that takes the current vertex ID and an EdgeTriplet
* between the vertex and one of its neighbors and produces a message to send
* to that neighbor.
* @param mergeMsg a user supplied function that takes two incoming messages of type A and merges
* them into a single message of type A. ''This function must be commutative and
* associative.''
* @param initialMsg the message each vertex will receive at the beginning of the
* first iteration.
* @param numIter the number of iterations to run this computation for.
*
* @return the resulting graph at the end of the computation
*
*/
def iterate[VD: ClassManifest, ED: ClassManifest, A: ClassManifest](graph: Graph[VD, ED])(
vprog: (Vid, VD, A) => VD,
sendMsg: (Vid, EdgeTriplet[VD, ED]) => Option[A],

55
graph/src/main/scala/org/apache/spark/graph/impl/GraphImpl.scala
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@ -261,71 +261,27 @@ class GraphImpl[VD: ClassManifest, ED: ClassManifest] protected (
}
// Because of the edgepartitioner, we know that all edges with the same src and dst
// will be in the same partition
// We will want to keep the same partitioning scheme. Use newGraph() rather than
// new GraphImpl()
// TODO(crankshaw) is there a better way to do this using RDD.groupBy()
// functions?
override def groupEdgeTriplets[ED2: ClassManifest](
f: Iterator[EdgeTriplet[VD,ED]] => ED2 ): Graph[VD,ED2] = {
//override def groupEdges[ED2: ClassManifest](f: Iterator[Edge[ED]] => ED2 ):
// I think that
// myRDD.mapPartitions { part =>
// val (vmap, edges) = part.next()
// gives me access to the vertex map and the set of
// edges within that partition
// This is what happens during mapPartitions
// The iterator iterates over all partitions
// val result: RDD[U] = new RDD[T]().mapPartitions(f: Iterator[T] => Iterator[U])
// TODO(crankshaw) figure out how to actually get the new Edge RDD and what
// type that should have
val newEdges: RDD[Edge[ED2]] = triplets.mapPartitions { partIter =>
// toList lets us operate on all EdgeTriplets in a single partition at once
partIter
// TODO(crankshaw) toList requires that the entire edge partition
// can fit in memory right now.
.toList
// groups all ETs in this partition that have the same src and dst
// Because all ETs with the same src and dst will live on the same
// partition due to the EdgePartitioner, this guarantees that these
// ET groups will be complete.
.groupBy { t: EdgeTriplet[VD, ED] => (t.srcId, t.dstId) }
//.groupBy { e => (e.src, e.dst) }
// Apply the user supplied supplied edge group function to
// each group of edges
// The result of this line is Map[(Long, Long, ED2]
.mapValues { ts: List[EdgeTriplet[VD, ED]] => f(ts.toIterator) }
// convert the resulting map back to a list of tuples
.toList
// TODO(crankshaw) needs an iterator over the tuples?
// Why can't I map over the list?
.toIterator
// map over those tuples that contain src and dst info plus the
// new edge data to make my new edges
.map { case ((src, dst), data) => Edge(src, dst, data) }
// How do I convert from a scala map to a list?
// I want to be able to apply a function like:
// f: (key, value): (K, V) => result: [R]
// so that I can transfrom a Map[K, V] to List[R]
// Maybe look at collections.breakOut
// see http://stackoverflow.com/questions/1715681/scala-2-8-breakout
// and http://stackoverflow.com/questions/6998676/converting-a-scala-map-to-a-list
}
// @todo eliminate the need to call createETable
//TODO(crankshaw) eliminate the need to call createETable
val newETable = createETable(newEdges,
eTable.index.partitioner.numPartitions)
new GraphImpl(vTable, vid2pid, localVidMap, newETable)
}
@ -340,7 +296,7 @@ class GraphImpl[VD: ClassManifest, ED: ClassManifest] protected (
.toIterator
.map { case ((src, dst), data) => Edge(src, dst, data) }
}
// @todo eliminate the need to call createETable
// TODO(crankshaw) eliminate the need to call createETable
val newETable = createETable(newEdges,
eTable.index.partitioner.numPartitions)
@ -654,7 +610,8 @@ object GraphImpl {
/**
* @todo(crankshaw) how does this effect load balancing?
* @todo This will only partition edges to the upper diagonal
* of the 2D processor space.
*/
protected def canonicalEdgePartitionFunction2D(srcOrig: Vid, dstOrig: Vid,
numParts: Pid, ceilSqrtNumParts: Pid): Pid = {

8
graph/src/main/scala/org/apache/spark/graph/util/GraphGenerators.scala
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@ -16,9 +16,9 @@ import org.apache.spark.graph.Graph
import org.apache.spark.graph.Edge
import org.apache.spark.graph.impl.GraphImpl
// TODO(crankshaw) I might want to pull at least RMAT out into a separate class.
// Might simplify the code to have classwide variables and such.
/**
* @todo(crankshaw) cleanup and modularize code
*/
object GraphGenerators {
val RMATa = 0.45
@ -236,8 +236,6 @@ object GraphGenerators {
}
}
}